Research Publications

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4194

This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.

Browse

Filters

2023 - 20263

Advanced Search

Filter by

Settings

Subject: search.filters.subject.SARIMA

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationEmbargo
    Enhancing the effectiveness of satellite precipitation products with topographic and seasonal bias correction
    (Elsevier B.V., 2026-02) Wanniarachchi, S; Sarukkalige, R; Hapuarachchi, H.A. P; Gomes, P.I.A; Rathnayake, U
    Estimating precipitation distribution across large regions is crucial for understanding water availability, planning infrastructure, and forecasting flood hazards. Traditional gauge-based methods face challenges, particularly with sparse gauge networks. In response, satellite-based, near-real-time (NRT) precipitation data has gained popularity, especially in poorly gauged watersheds. However, satellite precipitation data quality is often compromised by latency, atmospheric complexities, and topographic effects, resulting in nonlinear errors. To overcome the research gap, this study introduces the Heavy Rain Peak Adjustment (HRPA) method alongside the well-established Seasonal Autoregressive Integrated Moving Average (SARIMA) model for satellite precipitation bias correction. The analysis utilised Global Satellite Mapping of Precipitation (GSMaP-NRT) data and hourly precipitation records from 31 rain gauges in the Ovens River region of Australia. On average, the mean residual of observed and GSMaP-NRT precipitation was −0.02 mm. Additionally, the HRPA method yielded better linear regression R2(0.911), NSE (log) (−0.847), and RMSE (0.628) compared to SARIMA. The results indicate that HRPA outperforms SARIMA, particularly at lower elevations, whereas SARIMA struggles at higher elevations, underscoring its limitations in those areas. Additionally, autocorrelation and partial autocorrelation plots for some stations in hilly areas show significant wave-like patterns, indicating greater uncertainty in satellite precipitation estimates over complex terrain. For several stations, autocorrelations at 24 and 48-hour lags suggest a systematic influence of past residuals on future ones, emphasizing the need for further refinement in satellite precipitation correction methods for these regions.
  • Thumbnail Image
    PublicationOpen Access
    Predictive Model for Monthly Made Tea Production in Sri Lanka
    (Department of Mathematics and Statistics, Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Subasinghe, C; Wattegedara, N; Silva, T; Balasooriya, S; Dassanayake, K; Guruge, M.L
    This study forecasts monthly tea production in Sri Lanka by developing a suitable time series model to identify future trends in the national tea industry. The analysis is based on monthly made tea production data from January 2000 to June 2025, obtained from the Central Bank of Sri Lanka and the Sri Lanka Tea Board. After confirming the non-stationarity of the original series through the Augmented Dickey-Fuller test, both first-order and seasonal differencing were applied to achieve stationarity. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plotswere used to identify potential model structures.
  • Thumbnail Image
    PublicationOpen Access
    Development of SARIMA Model to Predict Quarterly Apparel and Textile Export Revenue in Sri Lanka
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Piyasiri, K. G. V.; Kasthuriarachchi, U. P; Nirmani, K. G. R; Tilakaratne, K.I.; Peiris, T. S. G
    Apparel and textile exports play a significant role in the Sri Lankan economy. The USA, UK, Italy, Germany, and Belgium are the main markets of apparel and textile exports in Sri Lanka. Advanced knowledge of export revenue is vital important for various reasons. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model of the type (1,1,0) x (0,1,1)4 was developed to model apparel and textile export revenue in Sri Lanka using quarterly data from year 2004 quarter 1 (2004Q1) to year 2021 quarter 4 (2021Q4). The errors of the model were found to be random and have a constant variance. The best fitted model was identified by comparing various statistical indicators, namely, the Akaike info criterion, Schwarz criterion, Hannan-Quinn criterion, Log likelihood criterion and volatility of six possible models decided based on sample ACF and PACF of the stationary series. The model was validated for data from year 2022Q1 to 2023Q1. The Mean Absolute Percentage Error (MAPE) for the training data set and validation data set were 7.68% and 11.35% respectively. The predicted revenues (Mn USD) for the 2023Q2 to 2024Q4 are 1074.23, 1263.30, 1222.22, 1206.74, 1058.38, 1265.00 and 1216.58, respectively. The forecasted values for short-term periods can be effectively used by the decision makers for various activities. The model developed is easy to use and reliable.